Software programs have been written to run sequentially since the beginning days of software development. Steadily over time, computers have become much more powerful, with more processing power and memory to handle advanced operations. This trend has recently shifted away from ever-increasing single-processor clock rates towards an increase in the number of processors available in a single computer resulting in a corresponding shift away from sequential execution toward parallel execution. Software developers want to take advantage of improvements in computer processing power to enable their software programs to run faster as new hardware is adopted. With parallel hardware, software developers arrange for one or more tasks of a particular software program to be executed in parallel (also referred to as concurrently), so that the same logical operation can utilize many processors at one time to thereby deliver better performance as more processors are added to the computers on which such software runs.
When parallelizing previously-written sequential algorithms, it is often desirable to keep as much of the previous sequential program behavior as possible. However, typical parallel execution of existing sequential logic introduces new behavioral characteristics and presents problems that can introduce challenges into the migration from sequential to parallel algorithms.
For example, for many operators, a parallel worker can typically operate on its own subset of input data independently, as though other parallel workers do not exist. However, for some operators, such as search operators, the output produced by some of the parallel workers may be dependent on the output of some of the other parallel workers, or useful in concluding the search sooner. Using conventional techniques for such operators can result in inefficient performance, as well as incorrect results.